Classifiers are used in image processing to classify pixels or regions in an image into one of a number of predefined classes. For example, a classifier can be used to classify regions in an image of natural scenery into one of a number of classes such as leaves, grass, or sky. In the medical field, classifiers are used to classify regions in images of patients into different types of tissue, for example, abnormal or diseased tissue and normal tissue.
Many different types of classifiers have been developed including Bayesian classifiers, k-nearest neighbor classifiers, and neural network classifiers. Typically, a classifier is customized for a given classification problem by training the classifier to identify particular classes. This usually involves presenting the classifier with a set of example image regions that are representative of known classes. The classifier extracts features from the set of example image regions and learns to associate these features with the known classes based on association rules. Once the classifier has been trained to identify the classes, the classifier can be used to identify occurrences of these classes in new images.
In practice, classifiers misclassify image regions some of the time. Therefore, there is a need to improve classification accuracy.